Control theory-based data assimilation for open channel hydraulic models: tuning PID controllers using multi-objective optimization

Abstract Reliable water resources management requires decision support tools to successfully forecast hydraulic data (stage and flow hydrographs). Even though data-driven methods are nowadays trendy apply, they still fail provide reliable forecasts during extreme periods due a lack of training data. Therefore, model-driven forecasting is needed. However, the approach affected by numerous uncertainties in initial boundary conditions. To improve real-time model's operation, it can be regularly updated using measured assimilation (DA) procedure. Widely used DA techniques computationally expensive, which reduce their applications. Previous research shows that tailor-made, time-efficient based on control theory could instead. This paper presents further insights into theory-based for 1D models. method uses Proportional–Integrative–Derivative (PID) controllers assimilate computed levels observed describes two-stage PID controllers’ tuning Multi-objective optimization Nondominated Sorting Genetic Algorithm II (NSGA-II) was determine optimal parameters controllers. The proposed procedure tested model as tool transboundary Iron Gate 1 hydropower system Danube River, showing average discrepancy between modeled less than 0.05 m more 97% window.